Socio-demographic and health factors drive the epidemic progression and should guide vaccination strategies for best COVID-19 containment

Influence of waning immunity on vaccination decision-making: A multi-strain epidemic model with an evolutionary approach analyzing cost and efficacy

In this research, we introduce a comprehensive epidemiological model that accounts for multiple strains of an infectious disease and two distinct vaccination options. Vaccination stands out as the most effective means to prevent and manage infectious diseases. However, when there are various vaccines available, each with its costs and effectiveness, the decision-making process for individuals becomes paramount. Furthermore, the factor of waning immunity following vaccination also plays a significant role in influencing these choices. To understand how individuals make decisions in the context of multiple strains and waning immunity, we employ a behavioral model, allowing an epidemiological model to be coupled with the dynamics of a decision-making process. Individuals base their choice of vaccination on factors such as the total number of infected individuals and the cost-effectiveness of the vaccine. Our findings indicate that as waning immunity increases, people tend to prioritize vaccines with higher costs and greater efficacy. Moreover, when more contagious strains are present, the equilibrium in vaccine adoption is reached more rapidly. Finally, we delve into the social dilemma inherent in our model by quantifying the social efficiency deficit (SED) under various parameter combinations.

Dynamics of respiratory infectious diseases under rapid urbanization and COVID-19 pandemic in the subcenter of Beijing during 2014-2022

Objective

The study analyzed the impact of urbanization on epidemiological characteristics of respiratory infectious disease in Tongzhou District, Beijing during 2014-2022 to provide reference for prevention and control priorities of respiratory infectious diseases during the innovative urbanization process in China.

Methods

The incidence data of notifiable respiratory infectious diseases (NRIDs) in Tongzhou Beijing during 2014-2022 were summarized. The trend of incidence rate was analyzed by Joinpoint regression model, and entropy method was performed to construct the comprehensive index of urbanization (CIU) and generalized linear model was used to analyze the influence of CIU on the incidence rate of respiratory infectious diseases.

Results

Totally 72616 NRIDs cases were reported in Tongzhou District during 2014-2022, and the incidence rate of NRIDs was higher during 2017-2019 (153/100 000) than during 2014-2016 (930/100 000) and during 2020-2022 (371/100 000), respectively (both P < 0.001). The CIU constantly increased with slight fluctuation in 2016 and 2018, respectively. The incidence rate of NRIDs showed an increase along with the CIU during 2014-2019 (r = 0.95, P = 0.004), while the incidence rate's tendency was interrupted by COVID-19 during 2020 with slight decrease in 2020-2021 and rebounded in 2022. For the patients aged <15 years, the incidence rate of NRIDs revealed a very sharp rise at the urbanization period without COVID-19 pandemic compared with that under pre-urbanization period (RR = 7.93, 95 % CI 7.63-8.24), and dropped off to the similar level as of pre-urbanization period when COVID-19 pandemic spread.

Conclusions

Urbanization process may increase the incidence of NRIDs but constrained by COVID-19. Certain measures should be taken to prevent and control the effects by urbanization process, such as good natural environment with less population density, ecological environment with good air quality, promoted hand hygiene, mask wearing, keeping interpersonal distance, vaccination, media publicity for NRIDs' prevention and control.

Association of socio-demographic factors with clinical outcome among hospitalized patients in first and second waves of COVID-19 pandemic: Study from the developing world

Background

Recent disease resurgence in China indicates that corona virus infectious disease is still a pertinent public health problem. We stand at a juncture where we are still unsure about the initial dilemmas regarding its birth, therapies, and the emerging novel strains. Medical literature has focused on the clinical, laboratory, radiological, and therapeutic aspects of disease management. There is paucity of literature on the association between socio-demographic variables on disease severity and clinical outcome.

Materials and Methods

This retrospective observational study analyzing the socio-demographic variables was performed at a dedicated COVID care center in western Maharashtra, India. Electronic records of all individuals who were admitted to this hospital from July 29 2020, to June 14, 2021, and diagnosed COVID-19 positive by reverse transcriptase polymerase chain reaction (RT-PCR) were identified after due institutional ethical clearance. Patients admitted from July 29, 2020, to February 27, 2021, were categorized as patients presenting during the 'first wave of viral pandemic'. Those admitted from March 01, 2021, to June 14, 2021, have been included as patients admitted during 'second wave of viral pandemic'. The following outcome parameters were collected (presenting symptoms, duration of symptoms before the individual presented for diagnostic RT-PCR, total duration of symptoms, severity of disease at onset, duration of hospital stay, the final outcome (discharge/death) and Charlson's comorbidity index). The linear regression model was used to establish association between socio-demographic factors and disease severity at onset (mild/moderate/severe/critical).

Results

A total of 37033 patients were screened, and the positivity rate with RT-PCR was 16.99% (n = 6275) during the study period. Out of which 45% (n = 2824) of the patients had mild disease requiring home isolation and the remaining 55% of patients required admission. 1590 patients from the first wave and 910 from the second wave of COVID-19 were hospitalized and included in the study after exclusion. The mean age of patients in first wave was 49 years and that in second wave was 54 years with 77.6% and 70.6% males in two waves, respectively. The burden of critical cases was higher in second wave as computed to first wave (10% vs 8%). The second wave had more outreach in the rural population as compared to second one (17.8% vs 12.2%). The mean duration from the onset of symptoms to hospitalization was 03 and 04 days, respectively, in two waves. Mortality associated in two waves was 11.9% and 24%, respectively (P < 0.05). Higher Charlson's comorbidity index was associated with higher mortality, and the cumulative survival from urban area was more as compared to the rural population (log rank - 9.148, P = 0.0002).

Conclusion

The second COVID-19 wave had significantly higher case mortality. It affected elderly patients and those with rural background. The factors associated with higher mortality during COVID-19 pandemic were rural background, higher Charlson's comorbidity index and late presentation to the hospital. Ongoing vaccine campaigns, thus, should focus on rural areas and individuals with comorbidities especially in developing and least developed countries.

Effective vaccination strategies for human papillomavirus (HPV) infection and cervical cancer based on the mathematical model with a stochastic process

Human papillomavirus (HPV) infection poses a significant risk to women's health by causing cervical cancer. In addition to HPV, cervical cancer incidence rates can be influenced by various factors, including human immunodeficiency virus and herpes, as well as screening policy. In this study, a mathematical model with stochastic processes was developed to analyze HPV transmission between genders and its subsequent impact on cervical cancer incidence. The model simulations suggest that both-gender vaccination is far more effective than female-only vaccination in preventing an increase in cervical cancer incidence. With increasing stochasticity, the difference between the number of patients in the vaccinated group and the number in the nonvaccinated group diminishes. To distinguish the patient population distribution of the vaccinated from the nonvaccinated, we calculated effect size (Cohen's distance) in addition to Student's t-test. The model analysis suggests a threshold vaccination rate for both genders for a clear reduction of cancer incidence when significant stochastic factors are present.

Vaccination strategies impact the probability of outbreak extinction: A case study of COVID-19 transmission

Mass vaccination has proven to be an effective control measure for mitigating the transmission of infectious diseases. Throughout history, various vaccination strategies have been employed to control infections and terminate outbreaks. In this study, we utilized the transmission of COVID-19 as a case study and constructed a stochastic age-structured compartmental model to investigate the effectiveness of different vaccination strategies. Our analysis focused on estimating the outbreak extinction probability under different vaccination scenarios in both homogeneous and heterogeneous populations. Notably, we found that population heterogeneity can enhance the likelihood of outbreak extinction at varying levels of vaccine coverage. Prioritizing vaccinations for individuals with higher infection risk was found to maximize outbreak extinction probability and reduce overall infections, while allocating vaccines to those with higher mortality risk has been proven more effective in reducing deaths. Moreover, our study highlighted the significance of booster doses as the vaccine effectiveness wanes over time, showing that they can significantly enhance the extinction probability and mitigate disease transmission.

Text analysis of Macao's COVID-19 prevention and control policies: discussion on strategy evolution and public health capabilities

Background

The global impact of the coronavirus disease 2019 (COVID-19) pandemic has been profound. Macao Special Administrative Region (SAR), renowned as an international hub for tourism and entertainment, has actively responded to the crisis. However, a comprehensive analysis detailing the evolution of Macao SAR's policies throughout this period is currently lacking.

Methods

This study aims to comprehensively understand the decision-making processes, policy formulation, and implementation strategies of the Macao SAR government amidst the pandemic through the analysis of speeches and inquiries made by legislative council members and other relevant documents. Employing both quantitative and qualitative analytical methods, including word frequency analysis and word vector models, we identify key themes and patterns. Additionally, we conducted a comparative analysis of keyword frequencies during the two waves of the pandemic using radar charts.

Results

The results indicate a heightened focus by the Macao SAR government on pandemic control measures and economic impacts. In response, the government formulated and implemented policies, provided support initiatives, and managed port clearance, all while focusing on enhancing healthcare infrastructure and community services.

Conclusions

The government persistently amends its policies in response to the evolving challenges posed by the pandemic. The evolution of the dynamic Zero-COVID strategy highlights the government's adaptability and comprehensive consideration, ensuring public health and societal stability.

Gender-Neutral HPV Vaccine in India; Requisite for a Healthy Community: A Review

Human papillomavirus (HPV) affects approximately 80% of individuals, irrespective of gender, and is implicated in various cancers. Existing HPV vaccines, while safe and effective, do not sufficiently protect males when administered solely to females. This review, triggered by the urgent need to address this gap and reduce the associated stigma, aims to evaluate the introduction of a gender-neutral HPV vaccine, GARDASIL-9, in India. The primary objective is to assess the necessity and feasibility of incorporating the gender-neutral HPV vaccine into India's national immunization program. This integration is crucial to ensure equitable access for all children and to mitigate the substantial burden of HPV. A literature search was conducted using databases such as Google Scholar, PubMed, government websites, and relevant publications. Keywords included "gender-neutral vaccine", "HPV vaccine", and "Indian population". The central research question guiding this review is: How necessary and feasible is the inclusion of a gender-neutral HPV vaccine in India's national immunization schedule to ensure equitable access for all children and reduce the HPV burden? The review inclusion criteria comprised studies addressing the prevalence of HPV infections, HPV vaccination awareness among both genders, the cost-effectiveness of gender-neutral vaccines, current HPV vaccination status, and future perspectives specific to India. Studies not meeting these criteria were excluded. The review highlights that introducing a gender-neutral HPV vaccine in India is imperative. Including males in vaccination efforts significantly reduces the overall disease burden and helps in reducing the stigma associated with HPV. A comprehensive vaccination program, bolstered by education and awareness campaigns, and its inclusion in the national immunization schedule is essential. This approach ensures equitable access to the vaccine for all children, fostering a healthier community, preventing HPV-related cancers, and enhancing public health outcomes in India.

Epidemic Spreading in Metapopulation Networks Coupled With Awareness Propagation

Understanding the feedback loop that links the spatiotemporal spread of infectious diseases and human behavior is an open problem. To study this problem, we develop a multiplex framework that couples epidemic spreading across subpopulations in a metapopulation network (i.e., physical layer) with the spreading of awareness about the epidemic in a communication network (i.e., virtual layer). We explicitly study the interactions between the mobility patterns across subpopulations and the awareness propagation among individuals. We analyze the coupled dynamics using microscopic Markov chains (MMCs) equations and validate the theoretical results via Monte Carlo (MC) simulations. We find that with the spreading of awareness, reducing human mobility becomes more effective in mitigating the large-scale epidemic. We also investigate the influence of varying topological features of the physical and virtual layers and the correlation between the connectivity and local population size per subpopulation. Overall the proposed modeling framework and findings contribute to the growing literature investigating the interplay between the spatiotemporal spread of epidemics and human behavior.

A deep-SIQRV epidemic model for COVID-19 to access the impact of prevention and control measures

The coronavirus (COVID-19) has mutated into several variants, and evidence says that new variants are more transmissible than existing variants. Even with full-scale vaccination efforts, the theoretical threshold for eradicating COVID-19 appears out of reach. This article proposes an artificial intelligence(AI) based intelligent prediction model called Deep-SIQRV(Susceptible-Infected-Quarantined-Recovered-Vaccinated) to simulate the spreading of COVID-19. While many models assume that vaccination provides lifetime protection, we focus on the impact of waning immunity caused by the conversion of vaccinated individuals back to susceptible ones. Unlike existing models, which assume that all coronavirus-infected individuals have the same infection rate, the proposed model considers the various infection rates to analyze transmission laws and trends. Next, we consider the influence of prevention and control strategies, such as media marketing and law enforcement, on the spread of the epidemic. We employed the PAN-LDA model to extract features from COVID-19-related discussions on social media and online news articles. Moreover, the Long Short Term Memory(LSTM) model and Evolution Strategies(ES) are used to optimize transmission rates of infection and other model parameters, respectively. The experimental results on epidemic data from various Indian states demonstrate that persons infected with coronavirus had a more significant infection rate within four to nine days after infection, which corresponds to the actual transmission laws of the epidemic. The experimental results show that the proposed model has good prediction ability and obtains the Mean Absolute Percentage Error(MAPE) of 0.875%, 0.965%, 0.298%, and 0.215% for the next eight days in Maharashtra, Kerala, Karnataka, and Delhi, respectively. Our findings highlight the significance of using vaccination data, COVID-19-related posts, and information generated by the government's tremendous efforts in the prediction calculation process.

Predictions from standard epidemiological models of consequences of segregating and isolating vulnerable people into care facilities

OBJECTIVES

Since the declaration of the COVID-19 pandemic, many governments have imposed policies to reduce contacts between people who are presumed to be particularly vulnerable to dying from respiratory illnesses and the rest of the population. These policies typically address vulnerable individuals concentrated in centralized care facilities and entail limiting social contacts with visitors, staff members, and other care home residents. We use a standard epidemiological model to investigate the impact of such circumstances on the predicted infectious disease attack rates, for interacting robust and vulnerable populations.

METHODS

We implement a general susceptible-infectious-recovered (SIR) compartmental model with two populations: robust and vulnerable. The key model parameters are the per-individual frequencies of within-group (robust-robust and vulnerable-vulnerable) and between-group (robust-vulnerable and vulnerable-robust) infectious-susceptible contacts and the recovery times of individuals in the two groups, which can be significantly longer for vulnerable people.

RESULTS

Across a large range of possible model parameters including degrees of segregation versus intermingling of vulnerable and robust individuals, we find that concentrating the most vulnerable into centralized care facilities virtually always increases the infectious disease attack rate in the vulnerable group, without significant benefit to the robust group.

CONCLUSIONS

Isolated care homes of vulnerable residents are predicted to be the worst possible mixing circumstances for reducing harm in epidemic or pandemic conditions.

SIRSi-vaccine dynamical model for the Covid-19 pandemic

Covid-19, caused by severe acute respiratory syndrome coronavirus 2, broke out as a pandemic during the beginning of 2020. The rapid spread of the disease prompted an unprecedented global response involving academic institutions, regulatory agencies, and industries. Vaccination and nonpharmaceutical interventions including social distancing have proven to be the most effective strategies to combat the pandemic. In this context, it is crucial to understand the dynamic behavior of the Covid-19 spread together with possible vaccination strategies. In this study, a susceptible-infected-removed-sick model with vaccination (SIRSi-vaccine) was proposed, accounting for the unreported yet infectious. The model considered the possibility of temporary immunity following infection or vaccination. Both situations contribute toward the spread of diseases. The transcritical bifurcation diagram of alternating and mutually exclusive stabilities for both disease-free and endemic equilibria were determined in the parameter space of vaccination rate and isolation index. The existing equilibrium conditions for both points were determined in terms of the epidemiological parameters of the model. The bifurcation diagram allowed us to estimate the maximum number of confirmed cases expected for each set of parameters. The model was fitted with data from São Paulo, the state capital of SP, Brazil, which describes the number of confirmed infected cases and the isolation index for the considered data window. Furthermore, simulation results demonstrate the possibility of periodic undamped oscillatory behavior of the susceptible population and the number of confirmed cases forced by the periodic small-amplitude oscillations in the isolation index. The main contributions of the proposed model are as follows: A minimum effort was required when vaccination was combined with social isolation, while additionally ensuring the existence of equilibrium points. The model could provide valuable information for policymakers, helping define disease prevention mitigation strategies that combine vaccination and non-pharmaceutical interventions, such as social distancing and the use of masks. In addition, the SIRSi-vaccine model facilitated the qualitative assessment of information regarding the unreported infected yet infectious cases, while considering temporary immunity, vaccination, and social isolation index.

Evaluating vaccine allocation strategies using simulation-assisted causal modeling

We develop a model to retrospectively evaluate age-dependent counterfactual vaccine allocation strategies against the coronavirus disease 2019 (COVID-19) pandemic. To estimate the effect of allocation on the expected severe-case incidence, we employ a simulation-assisted causal modeling approach that combines a compartmental infection-dynamics simulation, a coarse-grained causal model, and literature estimates for immunity waning. We compare Israel's strategy, implemented in 2021, with counterfactual strategies such as no prioritization, prioritization of younger age groups, or a strict risk-ranked approach; we find that Israel's implemented strategy was indeed highly effective. We also study the impact of increasing vaccine uptake for given age groups. Because of its modular structure, our model can easily be adapted to study future pandemics. We demonstrate this by simulating a pandemic with characteristics of the Spanish flu. Our approach helps evaluate vaccination strategies under the complex interplay of core epidemic factors, including age-dependent risk profiles, immunity waning, vaccine availability, and spreading rates.

A novel mathematical model for prioritization of individuals to receive vaccine considering governmental health protocols

Infectious diseases drive countries to provide vaccines to individuals. Due to the limited supply of vaccines, individuals prioritize receiving vaccinations worldwide. Although, priority groups are formed based on age groupings due to the restricted decision-making time. Governments usually ordain different health protocols such as lockdown policy, mandatory use of face masks, and vaccination during the pandemics. Therefore, this study considers the case of COVID-19 with a SEQIR (susceptible-exposed-quarantined-infected-recovered) epidemic model and presents a novel prioritization technique to minimize the social and economic impacts of the lockdown policy. We use retail units as one of the affected parts to demonstrate how a vaccination plan may be more effective if individuals such as retailers were prioritized and age groups. In addition, we estimate the total required vaccine doses to control the epidemic disease and compute the number of vaccine doses supplied by various suppliers. The vaccine doses are determined using optimal control theory in the solution technique. In addition, we consider the effect of the mask using policy in the number of vaccine doses allocated to each priority group. The model's performance is evaluated using an illustrative scenario based on a real case.

Computational modeling of fractional COVID-19 model by Haar wavelet collocation Methods with real data

This study explores the use of numerical simulations to model the spread of the Omicron variant of the SARS-CoV-2 virus using fractional-order COVID-19 models and Haar wavelet collocation methods. The fractional order COVID-19 model considers various factors that affect the virus's transmission, and the Haar wavelet collocation method offers a precise and efficient solution to the fractional derivatives used in the model. The simulation results yield crucial insights into the Omicron variant's spread, providing valuable information to public health policies and strategies designed to mitigate its impact. This study marks a significant advancement in comprehending the COVID-19 pandemic's dynamics and the emergence of its variants. The COVID-19 epidemic model is reworked utilizing fractional derivatives in the Caputo sense, and the model's existence and uniqueness are established by considering fixed point theory results. Sensitivity analysis is conducted on the model to identify the parameter with the highest sensitivity. For numerical treatment and simulations, we apply the Haar wavelet collocation method. Parameter estimation for the recorded COVID-19 cases in India from 13 July 2021 to 25 August 2021 has been presented.

SARS-CoV-2 seroprevalence and living conditions in Bamako (Mali): a cross-sectional multistage household survey after the first epidemic wave, 2020

OBJECTIVES

In low-income settings with limited access to diagnosis, COVID-19 information is scarce. In September 2020, after the first COVID-19 wave, Mali reported 3086 confirmed cases and 130 deaths. Most reports originated from Bamako, with 1532 cases and 81 deaths (2.42 million inhabitants). This observed prevalence of 0.06% appeared very low. Our objective was to estimate SARS-CoV-2 infection among inhabitants of Bamako, after the first epidemic wave. We assessed demographic, social and living conditions, health behaviours and knowledges associated with SARS-CoV-2 seropositivity.

SETTINGS

We conducted a cross-sectional multistage household survey during September 2020, in three neighbourhoods of the commune VI (Bamako), where 30% of the cases were reported.

PARTICIPANTS

We recruited 1526 inhabitants in 3 areas, that is, 306 households, and 1327 serological results (≥1 years), 220 household questionnaires and collected answers for 962 participants (≥12 years).

PRIMARY AND SECONDARY OUTCOME MEASURES

We measured serological status, detecting SARS-CoV-2 spike protein antibodies in blood sampled. We documented housing conditions and individual health behaviours through questionnaires among participants. We estimated the number of SARS-CoV-2 infections and deaths in the population of Bamako using the age and sex distributions.

RESULTS

The prevalence of SARS-CoV-2 seropositivity was 16.4% (95% CI 15.1% to 19.1%) after adjusting on the population structure. This suggested that ~400 000 cases and ~2000 deaths could have occurred of which only 0.4% of cases and 5% of deaths were officially reported. Questionnaires analyses suggested strong agreement with washing hands but lower acceptability of movement restrictions (lockdown/curfew), and mask wearing.

CONCLUSIONS

The first wave of SARS-CoV-2 spread broadly in Bamako. Expected fatalities remained limited largely due to the population age structure and the low prevalence of comorbidities. Improving diagnostic capacities to encourage testing and preventive behaviours, and avoiding the spread of false information remain key pillars, regardless of the developed or developing setting.

ETHICS

This study was registered in the registry of the ethics committee of the Faculty of Medicine and Odonto-Stomatology and the Faculty of Pharmacy, Bamako, Mali, under the number: 2020/162/CA/FMOS/FAPH.

Modeling COVID-19 transmission dynamics incorporating media coverage and vaccination

The COVID-19 pandemic has caused widespread concern around the world. In order to study the impact of media coverage and vaccination on the spread of COVID-19, we establish an SVEAIQR infectious disease model, and fit the important parameters such as transmission rate, isolation rate and vaccine efficiency based on the data from Shanghai Municipal Health Commission and the National Health Commission of the People's Republic of China. Meanwhile, the control reproduction number and the final size are derived. Moreover, through sensitivity analysis by PRCC (partial rank correlation coefficient), we discuss the effects of both the behavior change constant $ k $ according to media coverage and the vaccine efficiency $ \varepsilon $ on the transmission of COVID-19. Numerical explorations of the model suggest that during the outbreak of the epidemic, media coverage can reduce the final size by about 0.26 times. Besides that, comparing with $ 50\% $ vaccine efficiency, when the vaccine efficiency reaches $ 90\% $, the peak value of infected people decreases by about 0.07 times. In addition, we simulate the impact of media coverage on the number of infected people in the case of vaccination or non-vaccination. Accordingly, the management departments should pay attention to the impact of vaccination and media coverage.

Effect of vaccination patterns and vaccination rates on the spread and mortality of the COVID-19 pandemic

Objectives

Acquiring herd immunity through vaccination is the best way to curb the COVID-19 infection. Many countries have attempted to reach the herd immunity threshold as early as possible since the commencement of vaccination at the end of 2020. The purpose of this study is to (1) examine whether the pattern of vaccination rates affects the spread of COVID-19 and the consequent mortality and (2) investigate the level of cumulative vaccination rates that can begin to have an impact on reducing the spread and mortality of the pandemic.

Methods

This study selected 33 countries with higher vaccination rates as its sample set, classifying them into three groups as per vaccination patterns.

Results

The results showed that vaccination patterns have a significant impact on reducing spread and mortality. The full-speed vaccination pattern showed greater improvement in the spread of the COVID-19 pandemic than the other two patterns, while the striving vaccination pattern improved the most in terms of mortality. Secondly, the spread and mortality of the COVID pandemic started to significantly decline when the average cumulative vaccination rate reached 29.06 doses per 100 people and 7.88 doses per 100 people, respectively.

Conclusion

The study highlights the important role of vaccination patterns and the VTMR in reducing the epidemic spread and mortality.

Multi-layer network approach in modeling epidemics in an urban town

ABSTRACT

The last three years have been an extraordinary time with the COVID-19 pandemic killing millions, affecting and distressing billions of people worldwide. Authorities took various measures such as turning school and work to remote and prohibiting social relations via curfews. In order to mitigate the negative impact of the epidemics, researchers tried to estimate the future of the pandemic for different scenarios, using forecasting techniques and epidemics simulations on networks. Intending to better represent the real-life in an urban town in high resolution, we propose a novel multi-layer network model, where each layer corresponds to a different interaction that occurs daily, such as "household", "work" or "school". Our simulations indicate that locking down "friendship" layer has the highest impact on slowing down epidemics. Hence, our contributions are twofold, first we propose a parametric network generator model; second, we run SIR simulations on it and show the impact of layers.

Mining the vaccination willingness of China using social media data

OBJECTIVE

Vaccination is one of the most powerful and effective protective measures against Coronavirus disease 2019 (COVID-19). Currently, several blogs hold content on vaccination attitudes expressed on social media platforms, especially Sina Weibo, which is one of the largest social media platforms in China. Therefore, Weibo is a good data source for investigating public opinions about vaccination attitudes. In this paper, we aimed to effectively mine blogs to quantify the willingness of the public to get the COVID-19 vaccine.

MATERIALS AND METHODS

First, data including 144,379 Chinese blogs from Weibo, were collected between March 24 and April 28, 2021. The data were cleaned and preprocessed to ensure the quality of the experimental data, thereby reducing it to an experimental dataset of 72,496 blogs. Second, we employed a new fusion sentiment analysis model to analyze the sentiments of each blog. Third, the public's willingness to get the COVID-19 vaccine was quantified using the organic fusion of sentiment distribution and information dissemination effect.

RESULTS

(1) The intensity of bloggers' sentiment toward COVID-19 vaccines changed over time. (2) The extremum of positive and negative sentiment intensities occurred when hot topics related to vaccines appeared. (3) The study revealed that the public's willingness to get the COVID-19 vaccine and the actual vaccination doses shares a linear relationship.

CONCLUSION

We proposed a method for quantifying the public's vaccination willingness from social media data. The effectiveness of the method was demonstrated by a significant consistency between the estimates of public vaccination willingness and actual COVID-19 vaccination doses.

The Relationship between Daily Behavior Changes and Vaccine Attitudes at the Early Stage of the COVID-19 Pandemic among Japanese People from Different Demographics: A Retrospective and Exploratory Examination Using a Free-Response Survey

This study investigated how daily behaviors of Japanese people changed during the early stages of the COVID-19 pandemic and whether the change was mediated by demographics. It also examined whether the magnitude of behavior change in a demographic group is related to their attitudes towards the COVID-19 vaccine. 301 Japanese responded to an online survey in February 2021, in which they first wrote some activities they frequently performed before the virus outbreak and then wrote about activities in their current life. The number of gathered answers were 1858 for 'before' and 1668 for 'after', and they were grouped into 19 behavior categories. Overall, behaviors such as traveling, eating out, and shopping were much less frequently described in the 'after' condition; while housework, food delivery, and pandemic prevention were mentioned more. However, the change pattern was significantly influenced by demographics of age, gender, having children or not, and household income. Especially women, younger generations, and people without children showed the greatest extent of behavior change compared with the other demographic cohorts. These groups were reported to be vaccine-hesitant in the literature. This study suggests that individuals with hesitant attitudes towards vaccines are more willing to change their behaviors to control viral transmission.

Estimation of Total Cost Required in Controlling COVID-19 Outbreaks by Financial Incentives

In this article, we present a Monte Carlo simulation (MCS) to estimate the total cost required to control the spread of the COVID-19 pandemic by financial incentives. One of the greatest difficulties in controlling the spread of the COVID-19 pandemic is that most infected people are not identified and can transmit the virus to other people. Therefore, there is an urgent need to rapidly identify and isolate the infected people to avoid the further spread of COVID-19. To achieve this, we can consider providing a financial incentive for the people who voluntarily take the COVID-19 test and test positive. To prevent the abuse of the financial incentive policy, several conditions should be satisfied to receive the incentive. For example, an incentive is offered only if the recipients know who infected them. Based on the data obtained from epidemiological investigations, we calculated an estimated total cost of financial incentives for the policy by generating various possible infection routes using the estimated parameters and MCS. These results would help public health policymakers implement the proposed method to prevent the spread of the COVID-19 pandemic. In addition, the incentive policy can support various preparations such as hospital bed preparation, vaccine development, and so forth.

Retrospective Modeling of the Omicron Epidemic in Shanghai, China: Exploring the Timing and Performance of Control Measures

BACKGROUND

In late February 2022, the Omicron epidemic swept through Shanghai, and the Shanghai government responded to it by adhering to a dynamic zero-COVID strategy. In this study, we conducted a retrospective analysis of the Omicron epidemic in Shanghai to explore the timing and performance of control measures based on the eventual size and duration of the outbreak.

METHODS

We constructed an age-structured and vaccination-stratified SEPASHRD model by considering populations that had been detected or controlled before symptom onset. In addition, we retrospectively modeled the epidemic in Shanghai from 26 February 2022 to 31 May 2022 across four periods defined by events and interventions, on the basis of officially reported confirmed (58,084) and asymptomatic (591,346) cases.

RESULTS

According to our model fitting, there were about 785,123 positive infections, of which about 57,585 positive infections were symptomatic infections. Our counterfactual assessment found that precise control by grid management was not so effective and that citywide static management was still needed. Universal and enforced control by citywide static management contained 87.65% and 96.29% of transmission opportunities, respectively. The number of daily new and cumulative infections could be significantly reduced if we implemented static management in advance. Moreover, if static management was implemented in the first 14 days of the epidemic, the number of daily new infections would be less than 10.

CONCLUSIONS

The above research suggests that dynamic zeroing can only be achieved when strict prevention and control measures are implemented as early as possible. In addition, a lot of preparation is still needed if China wants to change its strategy in the future.

Three-layer model for the control of epidemic infection over multiple social networks

Abstract

This paper presents a hierarchical approach for controlling the spread of an epidemic disease. The approach consists of a three-layer architecture where a set of two-layer multiple social networks is governed by a (third) top-layer consisting of an optimal control policy. Each of the two-layer social networks is modeled by a microscopic Markov chain. On top of all the two-layer networks is an optimal control policy that has been developed by using an underlying Markov Decision Process (MDP) model. Mathematical models pertaining to the top-level MDP as well as two-layer microscopic Markov chains have been presented. Practical implementation methodology using the proposed models has also been discussed along with a numerical example. The results in the numerical example illustrate the control of an epidemic using the optimal policy. Directions for further research and characterization of the optimal policy have also been discussed with the help of the same numerical example.

Article Highlights

An optimal approach for controlling the spread of an epidemic infection.The approach is able to model the uncertainties involved in the problem.The approach is able to cater for the underlying social network.

Assessing the medical resources in COVID-19 based on evolutionary game

COVID-19 has brought a great challenge to the medical system. A key scientific question is how to make a balance between home quarantine and staying in the hospital. To this end, we propose a game-based susceptible-exposed-asymptomatic -symptomatic- hospitalized-recovery-dead model to reveal such a situation. In this new framework, time-varying cure rate and mortality are employed and a parameter m is introduced to regulate the probability that individuals are willing to go to the hospital. Through extensive simulations, we find that (1) for low transmission rates (β < 0.2), the high value of m (the willingness to stay in the hospital) indicates the full use of medical resources, and thus the pandemic can be easily contained; (2) for high transmission rates (β > 0.2), large values of m lead to breakdown of the healthcare system, which will further increase the cumulative number of confirmed cases and death cases. Finally, we conduct the empirical analysis using the data from Japan and other typical countries to illustrate the proposed model and to test how our model explains reality.

Estimation of monkeypox spread in a nonendemic country considering contact tracing and self-reporting: A stochastic modeling study

In May 2022, monkeypox started to spread in nonendemic countries. To investigate contact tracing and self-reporting of the primary case in the local community, a stochastic model is developed. An algorithm based on Gillespie's stochastic chemical kinetics is used to quantify the number of infections, contacts, and duration from the arrival of the primary case to the detection of the index case (or until there are no more local infections). Different scenarios were set considering the delay in contact tracing and behavior of infectors. We found that the self-reporting behavior of a primary case is the most significant factor affecting outbreak size and duration. Scenarios with a self-reporting primary case have an 86% reduction in infections (average: 5-7, in a population of 10 000) and contacts (average: 27-72) compared with scenarios with a non-self-reporting primary case (average number of infections and contacts: 27-72 and 197-537, respectively). Doubling the number of close contacts per day is less impactful compared with the self-reporting behavior of the primary case as it could only increase the number of infections by 45%. Our study emphasizes the importance of the prompt detection of the primary case.

Occupations and their impact on the spreading of COVID-19 in urban communities

The current pandemic has disproportionally affected the workforce. To improve our understanding of the role that occupations play in the transmission of COVID-19, we analyse real-world network data that were collected in Bucharest between August 1st and October 31st 2020. The data record sex, age, and occupation of 6895 patients and the 13,272 people they have interacted with, thus providing a social network from an urban setting through which COVID-19 has spread. Quite remarkably, we find that medical occupations have no significant effect on the spread of the virus. Instead, we find common transmission chains to start with infected individuals who hold jobs in the private sector and are connected with non-active alters, such as spouses, siblings, or elderly relatives. We use relational hyperevent models to assess the most likely homophily and network effects in the community transmission. We detect homophily with respect to age and anti-homophily with respect to sex and employability. We note that, although additional data would be welcomed to perform more in-depth network analyses, our findings may help public authorities better target under-performing vaccination campaigns.

The COVID-19 pandemic as inspiration to reconsider epidemic models: A novel approach to spatially homogeneous epidemic spread modeling

Epidemic spread models are useful tools to study the spread and the effectiveness of the interventions at a population level, to an epidemic. The workhorse of spatially homogeneous class models is the SIR-type ones comprising ordinary differential equations for the unknown state variables. The transition between different states is expressed through rate functions. Inspired by -but not restricted to- features of the COVID-19 pandemic, a new framework for modeling a disease spread is proposed. The main concept refers to the assignment of properties to each individual person as regards his response to the disease. A multidimensional distribution of these properties represents the whole population. The temporal evolution of this distribution is the only dependent variable of the problem. All other variables can be extracted by post-processing of this distribution. It is noteworthy that the new concept allows an improved consideration of vaccination modeling because it recognizes vaccination as a modifier of individuals response to the disease and not as a means for individuals to totally defeat the disease. At the heart of the new approach is an infection age model engaging a sharp cut-off. This model is analyzed in detail, and it is shown to admit self-similar solutions. A hierarchy of models based on the new approach, from a generalized one to a specific one with three dominant properties, is derived. The latter is implemented as an example and indicative results are presented and discussed. It appears that the new framework is general and versatile enough to simulate disease spread processes and to predict the evolution of several variables of the population during this spread.

Modeling the Impact of Vaccination on COVID-19 and Its Delta and Omicron Variants

Vaccination is an important means to fight against the spread of the SARS-CoV-2 virus and its variants. In this work, we propose a general susceptible-vaccinated-exposed-infected-hospitalized-removed (SVEIHR) model and derive its basic and effective reproduction numbers. We set Hong Kong as an example and calculate conditions of herd immunity for multiple vaccines and disease variants. The model shows how the number of confirmed COVID-19 cases in Hong Kong during the second and third waves of the COVID-19 pandemic would have been reduced if vaccination were available then. We then investigate the relationships between various model parameters and the cumulative number of hospitalized COVID-19 cases in Hong Kong for the ancestral, Delta, and Omicron strains. Numerical results demonstrate that the static herd immunity threshold corresponds to one percent of the population requiring hospitalization or isolation at some point in time. We also demonstrate that when the vaccination rate is high, the initial proportion of vaccinated individuals can be lowered while still maintaining the same proportion of cumulative hospitalized/isolated individuals.

Epidemic dynamics on higher-dimensional small world networks

Dimension governs dynamical processes on networks. The social and technological networks which we encounter in everyday life span a wide range of dimensions, but studies of spreading on finite-dimensional networks are usually restricted to one or two dimensions. To facilitate investigation of the impact of dimension on spreading processes, we define a flexible higher-dimensional small world network model and characterize the dependence of its structural properties on dimension. Subsequently, we derive mean field, pair approximation, intertwined continuous Markov chain and probabilistic discrete Markov chain models of a COVID-19-inspired susceptible-exposed-infected-removed (SEIR) epidemic process with quarantine and isolation strategies, and for each model identify the basic reproduction number R 0 , which determines whether an introduced infinitesimal level of infection in an initially susceptible population will shrink or grow. We apply these four continuous state models, together with discrete state Monte Carlo simulations, to analyse how spreading varies with model parameters. Both network properties and the outcome of Monte Carlo simulations vary substantially with dimension or rewiring rate, but predictions of continuous state models change only slightly. A different trend appears for epidemic model parameters: as these vary, the outcomes of Monte Carlo change less than those of continuous state methods. Furthermore, under a wide range of conditions, the four continuous state approximations present similar deviations from the outcome of Monte Carlo simulations. This bias is usually least when using the pair approximation model, varies only slightly with network size, and decreases with dimension or rewiring rate. Finally, we characterize the discrepancies between Monte Carlo and continuous state models by simultaneously considering network efficiency and network size.

An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect

Currently, the spread of COVID-19 is running at a constant pace. The current situation is not so alarming, but every pandemic has a history of three waves. Two waves have been seen, and now expecting the third wave. Compartmental models are one of the methods that predict the severity of a pandemic. An enhanced SEIR model is expected to predict the new cases of COVID-19. The proposed model has an additional compartment of vaccination. This proposed model is the SEIRV model that predicts the severity of COVID-19 when the population is vaccinated. The proposed model is simulated with three conditions. The first condition is when social distancing is not incorporated, while the second condition is when social distancing is included. The third one condition is when social distancing is combined when the population is vaccinated. The result shows an epidemic growth rate of about 0.06 per day, and the number of infected people doubles every 10.7 days. Still, with imparting social distancing, the proposed model obtained the value of R0 is 1.3. Vaccination of infants and kids will be considered as future work.

Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

A spatiotemporally resolved infection risk model for airborne transmission of COVID-19 variants in indoor spaces

The classic Wells-Riley model is widely used for estimation of the transmission risk of airborne pathogens in indoor spaces. However, the predictive capability of this zero-dimensional model is limited as it does not resolve the highly heterogeneous spatiotemporal distribution of airborne pathogens, and the infection risk is poorly quantified for many pathogens. In this study we address these shortcomings by developing a novel spatiotemporally resolved Wells-Riley model for prediction of the transmission risk of different COVID-19 variants in indoor environments. This modelling framework properly accounts for airborne infection risk by incorporating the latest clinical data regarding viral shedding by COVID-19 patients and SARS-CoV-2 infecting human cells. The spatiotemporal distribution of airborne pathogens is determined via computational fluid dynamics (CFD) simulations of airflow and aerosol transport, leading to an integrated model of infection risk associated with the exposure to SARS-CoV-2, which can produce quantitative 3D infection risk map for a specific SARS-CoV-2 variant in a given indoor space. Application of this model to airborne COVID-19 transmission within a hospital ward demonstrates the impact of different virus variants and respiratory PPE upon transmission risk. With the emergence of highly contagious SARS-CoV-2 variants such as the Delta and Omicron strains, respiratory PPE alone may not provide effective protection. These findings suggest a combination of optimal ventilation and respiratory PPE must be developed to effectively control the transmission of COVID-19 in healthcare settings and indoor spaces in general. This generalised risk estimation framework has the flexibility to incorporate further clinical data as such becomes available, and can be readily applied to consider a wide range of factors that impact transmission risk, including location and movement of infectious persons, virus variant and stage of infection, level of PPE and vaccination of infectious and susceptible individuals, impacts of coughing, sneezing, talking and breathing, and natural and mechanised ventilation and filtration.

The impact of the COVID-19 pandemic on outpatients of internal medicine and pediatrics: A descriptive study

ABSTRACT

This study analyzed the changes in the number of outpatients and disease presentation during the entirety of 2020, the period of COVID-19 pandemic.The average annual number of outpatient visits between 2017 and 2019 (before COVID-19) and the total number of outpatient visits in 2020 (COVID-19 period) were compared. Diagnostic codes were identified during 2 periods to analyze changes in the number of outpatient visits according to disease and month.The average annual number of outpatient visits was 47,105 before, and 40,786 during the COVID-19 pandemic, with a decrease of 13.4%. The number of outpatient visits in internal medicine decreased by 10.2% during the COVID-19 pandemic and tended to rebound during the second half of the year. However, the number of outpatient visits in the pediatric department decreased by 37.5% overall throughout the COVID-19 period and continued to decline in the second half of the year. The number of outpatients with infectious diseases decreased significantly (35.9%) compared to noninfectious diseases (cancer, 5.0%; circulatory disease, 4.1%). In addition, the number of outpatient visits due to viral diseases continued to decline, while the incidence of bacterial diseases increased rapidly in the second half of the year.This study confirmed that the number of outpatient visits due to bacterial or viral infections decreased throughout the COVID-19 crisis. Therefore, expanding public health and telemedicine services is necessary to prevent secondary health problems caused by essential medical use restrictions.

Factors that most expose countries to COVID-19: a composite indicators-based approach

Studies carried out in different countries correlate social, economic, environmental, and health factors with the number of cases and deaths from COVID-19. However, such studies do not reveal which factors make one country more exposed to COVID-19 than other. Based on the composite indicators approach, this research identifies the factors that most impact the number of cases and deaths of COVID-19 worldwide and measures countries' exposure to COVID-19. Three composite indicators of exposure to COVID-19 were constructed through Principal Component Analysis, Simple Additive Weighting, and k-means clustering. The number of cases and deaths from COVID-19 is strongly correlated ( R  > 0.60) with composite indicator scores and moderately concordant ( K  > 0.4) with country clusters. Factors directly or indirectly associated with the age of the population are the ones that most expose countries to COVID-19. The population of countries most exposed to COVID-19 is 12 years older on average. The proportion of the elderly population in these countries is at least twice that of countries less exposed to COVID-19. Factors that can increase the population's life expectancy, such as Gross Domestic Product per capita and the Human Development Index, are four times and 1.3 times higher in more exposed countries to COVID-19. Providing better living conditions increases both the population's life expectancy and the country's exposure to COVID-19.

A Vaccination Simulator for COVID-19: Effective and Sterilizing Immunization Cases

In this work, we present a particle-based SEIR epidemic simulator as a tool to assess the impact of different vaccination strategies on viral propagation and to model sterilizing and effective immunization outcomes. The simulator includes modules to support contact tracing of the interactions amongst individuals and epidemiological testing of the general population. The particles are distinguished by age to represent more accurately the infection and mortality rates. The tool can be calibrated by region of interest and for different vaccination strategies to enable locality-sensitive virus mitigation policy measures and resource allocation. Moreover, the vaccination policy can be simulated based on the prioritization of certain age groups or randomly vaccinating individuals across all age groups. The results based on the experience of the province of Lecco, Italy, indicate that the simulator can evaluate vaccination strategies in a way that incorporates local circumstances of viral propagation and demographic susceptibilities. Further, the simulator accounts for modeling the distinction between sterilizing immunization, where immunized people are no longer contagious, and effective immunization, where the individuals can transmit the virus even after getting immunized. The parametric simulation results showed that the sterilizing-age-based vaccination scenario results in the least number of deaths. Furthermore, it revealed that older people should be vaccinated first to decrease the overall mortality rate. Also, the results showed that as the vaccination rate increases, the mortality rate between the scenarios shrinks.

Modelling the impact of social distancing and targeted vaccination on the spread of COVID-19 through a real city-scale contact network

We use mobile device data to construct empirical interpersonal physical contact networks in the city of Portland, Oregon, both before and after social distancing measures were enacted during the COVID-19 pandemic. These networks reveal how social distancing measures and the public's reaction to the incipient pandemic affected the connectivity patterns within the city. We find that as the pandemic developed there was a substantial decrease in the number of individuals with many contacts. We further study the impact of these different network topologies on the spread of COVID-19 by simulating an SEIR epidemic model over these networks and find that the reduced connectivity greatly suppressed the epidemic. We then investigate how the epidemic responds when part of the population is vaccinated, and we compare two vaccination distribution strategies, both with and without social distancing. Our main result is that the heavy-tailed degree distribution of the contact networks causes a targeted vaccination strategy that prioritizes high-contact individuals to reduce the number of cases far more effectively than a strategy that vaccinates individuals at random. Combining both targeted vaccination and social distancing leads to the greatest reduction in cases, and we also find that the marginal benefit of a targeted strategy as compared to a random strategy exceeds the marginal benefit of social distancing for reducing the number of cases. These results have important implications for ongoing vaccine distribution efforts worldwide.

Numerical Investigations through ANNs for Solving COVID-19 Model

The current investigations of the COVID-19 spreading model are presented through the artificial neuron networks (ANNs) with training of the Levenberg-Marquardt backpropagation (LMB), i.e., ANNs-LMB. The ANNs-LMB scheme is used in different variations of the sample data for training, validation, and testing with 80%, 10%, and 10%, respectively. The approximate numerical solutions of the COVID-19 spreading model have been calculated using the ANNs-LMB and compared viably using the reference dataset based on the Runge-Kutta scheme. The obtained performance of the solution dynamics of the COVID-19 spreading model are presented based on the ANNs-LMB to minimize the values of fitness on mean square error (M.S.E), along with error histograms, regression, and correlation analysis.

Evaluation of the United States COVID-19 vaccine allocation strategy

BACKGROUND

Anticipating an initial shortage of vaccines for COVID-19, the Centers for Disease Control (CDC) in the United States developed priority vaccine allocations for specific demographic groups in the population. This study evaluates the performance of the CDC vaccine allocation strategy with respect to multiple potentially competing vaccination goals (minimizing mortality, cases, infections, and years of life lost (YLL)), under the same framework as the CDC allocation: four priority vaccination groups and population demographics stratified by age, comorbidities, occupation and living condition (congested or non-congested).

METHODS AND FINDINGS

We developed a compartmental disease model that incorporates key elements of the current pandemic including age-varying susceptibility to infection, age-varying clinical fraction, an active case-count dependent social distancing level, and time-varying infectivity (accounting for the emergence of more infectious virus strains). The CDC allocation strategy is compared to all other possibly optimal allocations that stagger vaccine roll-out in up to four phases (17.5 million strategies). The CDC allocation strategy performed well in all vaccination goals but never optimally. Under the developed model, the CDC allocation deviated from the optimal allocations by small amounts, with 0.19% more deaths, 4.0% more cases, 4.07% more infections, and 0.97% higher YLL, than the respective optimal strategies. The CDC decision to not prioritize the vaccination of individuals under the age of 16 was optimal, as was the prioritization of health-care workers and other essential workers over non-essential workers. Finally, a higher prioritization of individuals with comorbidities in all age groups improved outcomes compared to the CDC allocation.

CONCLUSION

The developed approach can be used to inform the design of future vaccine allocation strategies in the United States, or adapted for use by other countries seeking to optimize the effectiveness of their vaccine allocation strategies.

Vaccine-Associated Disease Enhancement (VADE): Considerations in Postvaccination COVID-19

Introduction

The COVID-19 pandemic has entered a new phase with the roll-out of several vaccines worldwide at an accelerated phase. The occurrence of a more severe presentation of COVID-19 after vaccination may affect policymakers' decision-making and vaccine uptake by the public. Vaccine-associated disease enhancement (VADE) is the modified presentation of infections in individuals after having received a prior vaccination. Currently, little is known about the potential of vaccine-associated disease enhancement (VADE) following COVID-19 immunization. Case Illustration. We herewith report two patients admitted with confirmed COVID-19 pneumonia with a history of CoronaVac vaccination. The first patient with a relatively milder course of the disease had received two doses of CoronaVac, whereas the second patient with a more progressive course of the disease received only one dose before developing symptoms and being admitted to the hospital. Our observations suggest that vaccination could act in boosting the inflammatory process and reveal the previously asymptomatic COVID-19 illness. Theoretically, vaccines could induce VADE, where only suboptimal, nonprotective titers of neutralizing antibodies were produced or proinflammatory T-helper type 2 response was induced. Secondly, enhanced respiratory disease (ERD) could manifest, where pulmonary symptoms are more severe due to peribronchial monocytic and eosinophilic infiltration. Understanding VADE is important for the decision-making by the public, clinicians, and policymakers and is warranted for successful vaccination uptake.

Conclusion

We report two cases of patients developing COVID-19 shortly after CoronaVac vaccination in which VADE is likely. We recommend that current vaccination strategies consider the measurement of neutralizing antibody titer as a guide in ensuring the safest strategy for mass immunization. Studies are needed to investigate the true incidence of VADE on vaccinated individuals as well as on how to differentiate between VADE and severe manifestations of COVID-19 that are unrelated to vaccination.

The role of age in the spreading of COVID-19 across a social network in Bucharest

We analyse officially procured data detailing the COVID-19 transmission in Romania's capital Bucharest between 1st August and 31st October 2020. We apply relational hyperevent models on 19,713 individuals with 13,377 infection ties to determine to what degree the disease spread is affected by age whilst controlling for other covariate and human-to-human transmission network effects. We find that positive cases are more likely to nominate alters of similar age as their sources of infection, thus providing evidence for age homophily. We also show that the relative infection risk is negatively associated with the age of peers, such that the risk of infection increases as the average age of contacts decreases. Additionally, we find that adults between the ages 35 and 44 are pivotal in the transmission of the disease to other age groups. Our results may contribute to better controlling future COVID-19 waves, and they also point to the key age groups which may be essential for vaccination given their prominent role in the transmission of the virus.